Application of three-class ROC analysis to task-based image quality assessment of simultaneous dual-isotope myocardial perfusion SPECT (MPS).
The diagnosis of cardiac disease using dual-isotope myocardial perfusion SPECT (MPS) is based on the defect status in both stress and rest images, and can be modeled as a three-class task of classifying patients as having no, reversible, or fixed perfusion defects. Simultaneous acquisition protocols for dual-isotope MPS imaging have gained much interest due to their advantages including perfect registration of the (201)Tl and (99m)Tc images in space and time, increased patient comfort, and higher clinical throughput. As a result of simultaneous acquisition, however, crosstalk contamination, where photons emitted by one isotope contribute to the image of the other isotope, degrades image quality. Minimizing the crosstalk is important in obtaining the best possible image quality. One way to minimize the crosstalk is to optimize the injected activity of the two isotopes by considering the three-class nature of the diagnostic problem. To effectively do so, we have previously developed a three-class receiver operating characteristic (ROC) analysis methodology that extends and unifies the decision theoretic, linear discriminant analysis, and psychophysical foundations of binary ROC analysis in a three-class paradigm. In this work, we applied the proposed three-class ROC methodology to the assessment of the image quality of simultaneous dual-isotope MPS imaging techniques and the determination of the optimal injected activity combination. In addition to this application, the rapid development of diagnostic imaging techniques has produced an increasing number of clinical diagnostic tasks that involve not only disease detection, but also disease characterization and are thus multiclass tasks. This paper provides a practical example of the application of the proposed three-class ROC analysis methodology to medical problems.
He
Xin
X
Department of Radiology, Johns Hopkins School of Medicine, 601 N. Caroline Street, Baltimore, MD 21287, USA. xinhe@jhmi.edu
Song
Xiyun
X
Frey
Eric C
EC
D002985:Clinical Protocols; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging / Q000503:physiopathology; D005080:Exercise Test; D005260:Female; D006321:Heart / Q000000981:diagnostic imaging / Q000503:physiopathology; D006801:Humans; D008297:Male; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D012372:ROC Curve; D011868:Radioisotopes / Q000097:blood; D012107:Research Design; D012146:Rest; D017256:Technetium Tc 99m Sestamibi; D013794:Thallium Radioisotopes; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*; D054928:Validation Studies as Topic
D011868:Radioisotopes; D013794:Thallium Radioisotopes; D017256:Technetium Tc 99m Sestamibi